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| Criterion |  Operations Causal Factor Analysis |  Product Discovery Smoke Test |  Operations MORT Analysis |  Growth A/B Testing |
|---|---|---|---|---|
Purposedifferent | For an event with a complicated course, the method breaks down the contributing factors along the timeline. It shows how conditions, decisions, and reactions together produce a course of events. | When demand only needs to be roughly verified, it tests interest with minimal effort. It measures whether people would take a next step at all. | For a safety-relevant event or a system with high protection requirements, the method examines where controls failed. It exposes both technical and organizational gaps. | When two variants compete, discussions quickly decide by taste rather than effect. A/B Testing checks behavior under controlled conditions and separates real improvement from chance or expectation effects. |
Complexitydifferent | High | Low | High | High |
Timedifferent | 2-6 h | 1-5 Tage | Mehrere Tage bis Wochen | 1-4 Wochen |
Participantsdifferent | 3-10 | Nutzertraffic | 2-6 | 1-6 |
Formatdifferent | Workshop + async | Async | Workshop + async | Async |
Outputdifferent | Event Timeline, Causal Factor Chart, Cause List, Corrective Actions | Interest Metrics, Conversion Signal, Learning Note | MORT Worksheets, Findings per Branch, Corrective Actions, Systemic Recommendations | Experiment results, Decision log, Learning summary |
Tagsno overlap | CausalityIncidentRoot causeTimeline | ValidationExperimentsDemandGrowth | Root causeSafetySystemicIncident | ExperimentsGrowthAnalyticsValidation |